What Are Machine Learning Pipelines? (And Why They Matter)

Everybody is jumping on the artificial intelligence (AI) craze. Whether its large language models, robotics or process automation, the world is going insane for the potential that AI can now offer. 

According to Globe Newswire, the global ML market size is expected to grow From £16.79 billion in 2022 to £166.43 billion by 2030 [1]

That’s why it’s essential that businesses understand the impact and importance of machine learning (ML) on their journey towards AI.

As a subset of AI, ML is the ultimate string-puller for most of these powerful tools and features. Without it, everything would be a lot slower and a lot less efficient.

(If you’d like to know more about ML, check out our blog!)

For ML to work smoothly, it requires a list of very important steps that ensure the comfortable deployment and scale of those complex predictive models. So what are ML pipelines? Why are they important? And what will happen if I don’t build them out?

What Are Machine Learning Pipelines? 

A ML pipeline simplifies the process of building ML models by breaking it down into several steps. 

These steps are essentially a series of clear directions taken to develop, deploy, and monitor a ML model, ultimately automating the end-to-end workflow of the model. 

And it isn’t a simple process to get going. 

From data collection, to data preparation, model training, evaluation, and deployment, the technical know-how to get an ML pipeline running smoothly can be difficult to attain. 

This means that for an effective end-to-end ML pipeline, you would require different roles and expertise for it to operate.

For example, data engineers focus on the data-related tasks. Data scientists concentrate on model training and evaluation. ML engineers focus on model deployment and workflow automation. 

This is some of the technical talent that a business would require to operationalise a ML pipeline: 

Business Analysts: are responsible for understanding business requirements and translating them into system specifications. They work closely with other team members to ensure that the ML pipeline is aligned with the organisation’s goals and objectives.

Data Engineers: are responsible for ensuring that the ML systems have a reliable source of clean and accessible data to learn from. They work on data ingestion, preprocessing, and storage, as well as managing data pipelines to ensure smooth data flow throughout the machine learning pipeline.

Machine Learning Engineers: develop, train, and tune ML models according to the pipeline’s requirements. They work closely with data scientists and other team members to optimise the model’s performance and ensure its successful deployment in production.

Data Scientists: work on the analysis and interpretation of data, development of ML models, and evaluation of their performance. They collaborate with other team members to refine the pipeline and improve the accuracy and efficiency of the models.

Without One, These Are The Downsides

Failing to build an efficient ML pipeline can have significant drawbacks for your business.

Increased Costs: Inefficient pipelines can be costly in terms of time and resources. Longer training times and increased data storage requirements can result in higher infrastructure costs.

Poor Performance: Slow pipelines can lead to poor performance and inaccurate models. This can have negative consequences for your business, such as inaccurate predictions or recommendations.

Difficulty Scaling: Inefficient pipelines can be challenging to scale as your business grows. This can limit your ability to leverage the insights from your machine learning models.

Lack of standardisation: the development and deployment process of ML models may not be standardised, leading to inefficiencies and inconsistencies.

Difficulty in reusing: challenges in reusing the different stages of the ML process for use with a new model, leading to inefficiencies and a waste of resources.

Building an efficient ML pipeline can help you:

  • obtain faster results,
  • achieve better accuracy, and
  • scale your projects as your business grows. 

On the other hand, failing to build an efficient pipeline can result in increased costs, poor performance, and difficulty scaling. As such, it’s important to invest time and resources in building an efficient pipeline that meets the demands of your business.

Benefits of Building an Efficient ML Pipeline

Building an efficient ML pipeline is critical to the success of any ML project. 

An efficient pipeline ensures that your models are trained accurately and quickly, allowing you to gain insights into your data faster. 

Here are some key benefits of building an efficient ML pipeline:

Faster Results: An efficient ML pipeline enables you to train models faster, allowing you to obtain results more quickly. This can be particularly important in industries where real-time decisions are necessary.

Improved Accuracy: An efficient pipeline can help you achieve better accuracy in your models. By reducing the time spent on model training, you can focus more on optimising your model architecture and hyperparameters.

Scalability: An efficient pipeline can handle larger volumes of data. This makes it easier to scale your ML project as your business grows.

How To Get Started On (Or Improve) Your Own ML Journey

To get the best out of modern AI and ML technology, it’s critical to look at either attracting or contracting the correct talent to get your pipelines in production.

And with the talent shortage, it doesn’t make things any easier. 

That’s where we can help. As a leading ML and Data engineering business, we can get you on track to accelerating your ML model deployment and make AI work for your business.

Check out our ML engineering services and get in touch!

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